




"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class 				snake_case_			(					lowerCamelCase_	, lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       IFInpaintingSuperResolutionPipeline
   A_				      =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
   A_				      =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}			)
   A_				      =       PipelineTesterMixin.required_optional_params - {'''latents'''}







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       return self._get_superresolution_dummy_components()







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=0)				->   List[Any]:
       if str(lowerCamelCase_).startswith('''mps'''):
           UpperCamelCase              =	torch.manual_seed(lowerCamelCase_)
       else:
           UpperCamelCase              =	torch.Generator(device=lowerCamelCase_).manual_seed(lowerCamelCase_)

       UpperCamelCase              =	floats_tensor((1, 3, 1_6, 1_6)       ,					rng=random.Random(lowerCamelCase_)).to(lowerCamelCase_)
       UpperCamelCase              =	floats_tensor((1, 3, 3_2, 3_2)       ,					rng=random.Random(lowerCamelCase_)).to(lowerCamelCase_)
       UpperCamelCase              =	floats_tensor((1, 3, 3_2, 3_2)       ,					rng=random.Random(lowerCamelCase_)).to(lowerCamelCase_)

       UpperCamelCase              =	{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''image''': image,
           '''original_image''': original_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''output_type''': '''numpy''',
       }

       return inputs







   @unittest.skipIf(
       torch_device != '''cuda''' or not is_xformers_available()       ,					reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,					)
   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)







   def   UpperCAmelCase__			(		self)				->   Tuple:
       self._test_save_load_optional_components()







   @unittest.skipIf(torch_device != '''cuda'''       ,					reason='''float16 requires CUDA''')
   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1e-1)







   def   UpperCAmelCase__			(		self)				->   List[str]:
       self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)







   def   UpperCAmelCase__			(		self)				->   int:
       self._test_save_load_local()







   def   UpperCAmelCase__			(		self)				->   int:
       self._test_inference_batch_single_identical(
           expected_max_diff=1e-2       ,					)







'''simple docstring'''







def       __UpperCamelCase					(      lowercase_       :  list[int]   ,       lowercase_       :  list[int]   ,       lowercase_       :  int   ):
       """simple docstring"""




       return not any(
           neighbour == 1 and colored_vertices[i] == color
           for i, neighbour in enumerate(lowercase_   )   )


def       __UpperCamelCase					(      lowercase_       :  list[list[int]]   ,       lowercase_       :  int   ,       lowercase_       :  list[int]   ,       lowercase_       :  int   ):
       """simple docstring"""




       if index == len(lowercase_   ):
              return True

       # Recursive Step
       for i in range(lowercase_   ):
              if valid_coloring(graph[index]   ,       lowercase_   ,       lowercase_   ):
                     # Color current vertex
                     a_						     =			i
                     # Validate coloring
                     if util_color(lowercase_   ,       lowercase_   ,       lowercase_   ,       index + 1   ):
                            return True
                     # Backtrack
                     a_						     =			-1
       return False


def       __UpperCamelCase					(      lowercase_       :  list[list[int]]   ,       lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			[-1] * len(lowercase_   )

       if util_color(lowercase_   ,       lowercase_   ,       lowercase_   ,       0   ):
              return colored_vertices

       return []




